126 research outputs found
Exploring Global and Local Information for Anomaly Detection with Normal Samples
Anomaly detection aims to detect data that do not conform to regular
patterns, and such data is also called outliers. The anomalies to be detected
are often tiny in proportion, containing crucial information, and are suitable
for application scenes like intrusion detection, fraud detection, fault
diagnosis, e-commerce platforms, et al. However, in many realistic scenarios,
only the samples following normal behavior are observed, while we can hardly
obtain any anomaly information. To address such problem, we propose an anomaly
detection method GALDetector which is combined of global and local information
based on observed normal samples. The proposed method can be divided into a
three-stage method. Firstly, the global similar normal scores and the local
sparsity scores of unlabeled samples are computed separately. Secondly,
potential anomaly samples are separated from the unlabeled samples
corresponding to these two scores and corresponding weights are assigned to the
selected samples. Finally, a weighted anomaly detector is trained by loads of
samples, then the detector is utilized to identify else anomalies. To evaluate
the effectiveness of the proposed method, we conducted experiments on three
categories of real-world datasets from diverse domains, and experimental
results show that our method achieves better performance when compared with
other state-of-the-art methods.Comment: 6 pages, 1 figure
MeshNet: Mesh Neural Network for 3D Shape Representation
Mesh is an important and powerful type of data for 3D shapes and widely
studied in the field of computer vision and computer graphics. Regarding the
task of 3D shape representation, there have been extensive research efforts
concentrating on how to represent 3D shapes well using volumetric grid,
multi-view and point cloud. However, there is little effort on using mesh data
in recent years, due to the complexity and irregularity of mesh data. In this
paper, we propose a mesh neural network, named MeshNet, to learn 3D shape
representation from mesh data. In this method, face-unit and feature splitting
are introduced, and a general architecture with available and effective blocks
are proposed. In this way, MeshNet is able to solve the complexity and
irregularity problem of mesh and conduct 3D shape representation well. We have
applied the proposed MeshNet method in the applications of 3D shape
classification and retrieval. Experimental results and comparisons with the
state-of-the-art methods demonstrate that the proposed MeshNet can achieve
satisfying 3D shape classification and retrieval performance, which indicates
the effectiveness of the proposed method on 3D shape representation
Attention-based Multi-modal Fusion Network for Semantic Scene Completion
This paper presents an end-to-end 3D convolutional network named
attention-based multi-modal fusion network (AMFNet) for the semantic scene
completion (SSC) task of inferring the occupancy and semantic labels of a
volumetric 3D scene from single-view RGB-D images. Compared with previous
methods which use only the semantic features extracted from RGB-D images, the
proposed AMFNet learns to perform effective 3D scene completion and semantic
segmentation simultaneously via leveraging the experience of inferring 2D
semantic segmentation from RGB-D images as well as the reliable depth cues in
spatial dimension. It is achieved by employing a multi-modal fusion
architecture boosted from 2D semantic segmentation and a 3D semantic completion
network empowered by residual attention blocks. We validate our method on both
the synthetic SUNCG-RGBD dataset and the real NYUv2 dataset and the results
show that our method respectively achieves the gains of 2.5% and 2.6% on the
synthetic SUNCG-RGBD dataset and the real NYUv2 dataset against the
state-of-the-art method.Comment: Accepted by AAAI 202
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection
Graph anomaly detection plays a crucial role in identifying exceptional
instances in graph data that deviate significantly from the majority. It has
gained substantial attention in various domains of information security,
including network intrusion, financial fraud, and malicious comments, et al.
Existing methods are primarily developed in an unsupervised manner due to the
challenge in obtaining labeled data. For lack of guidance from prior knowledge
in unsupervised manner, the identified anomalies may prove to be data noise or
individual data instances. In real-world scenarios, a limited batch of labeled
anomalies can be captured, making it crucial to investigate the few-shot
problem in graph anomaly detection. Taking advantage of this potential, we
propose a novel few-shot Graph Anomaly Detection model called FMGAD (Few-shot
Message-Enhanced Contrastive-based Graph Anomaly Detector). FMGAD leverages a
self-supervised contrastive learning strategy within and across views to
capture intrinsic and transferable structural representations. Furthermore, we
propose the Deep-GNN message-enhanced reconstruction module, which extensively
exploits the few-shot label information and enables long-range propagation to
disseminate supervision signals to deeper unlabeled nodes. This module in turn
assists in the training of self-supervised contrastive learning. Comprehensive
experimental results on six real-world datasets demonstrate that FMGAD can
achieve better performance than other state-of-the-art methods, regardless of
artificially injected anomalies or domain-organic anomalies
Multitask Oriented Virtual Resource Integration and Optimal Scheduling in Cloud Manufacturing
To deal with the problem of resource integration and optimal scheduling in cloud manufacturing, based on the analyzation of the existing literatures, multitask oriented virtual resource integration and optimal scheduling problem is presented from the perspective of global optimization based on the consideration of sharing and correlation among virtual resources. The correlation models of virtual resources in a task and among tasks are established. According to the correlation model and characteristics of resource sharing, the formulation in which resource time-sharing scheduling strategy is employed is put forward, and then the formulation is simplified to solve the problem easily. The genetic algorithm based on the real number matrix encoding is proposed. And crossover and mutation operation rules are designed for the real number matrix. Meanwhile, the evaluation function with the punishment mechanism and the selection strategy with pressure factor are adopted so as to approach the optimal solution more quickly. The experimental results show that the proposed model and method are feasible and effective both in situation of enough resources and limited resources in case of a large number of tasks
Grow and Merge: A Unified Framework for Continuous Categories Discovery
Although a number of studies are devoted to novel category discovery, most of
them assume a static setting where both labeled and unlabeled data are given at
once for finding new categories. In this work, we focus on the application
scenarios where unlabeled data are continuously fed into the category discovery
system. We refer to it as the {\bf Continuous Category Discovery} ({\bf CCD})
problem, which is significantly more challenging than the static setting. A
common challenge faced by novel category discovery is that different sets of
features are needed for classification and category discovery: class
discriminative features are preferred for classification, while rich and
diverse features are more suitable for new category mining. This challenge
becomes more severe for dynamic setting as the system is asked to deliver good
performance for known classes over time, and at the same time continuously
discover new classes from unlabeled data. To address this challenge, we develop
a framework of {\bf Grow and Merge} ({\bf GM}) that works by alternating
between a growing phase and a merging phase: in the growing phase, it increases
the diversity of features through a continuous self-supervised learning for
effective category mining, and in the merging phase, it merges the grown model
with a static one to ensure satisfying performance for known classes. Our
extensive studies verify that the proposed GM framework is significantly more
effective than the state-of-the-art approaches for continuous category
discovery.Comment: This paper has already been accepted by 36th Conference on Neural
Information Processing Systems (NeurIPS 2022
Convergence of resistance and evolutionary responses in Escherichia coli and Salmonella enterica co-inhabiting chicken farms in China
Sharing of genetic elements among different pathogens and commensals inhabiting same hosts and environments has significant implications for antimicrobial resistance (AMR), especially in settings with high antimicrobial exposure. We analysed 661 Escherichia coli and Salmonella enterica isolates collected within and across hosts and environments, in 10 Chinese chicken farms over 2.5 years using novel data-mining methods. Most isolates within same hosts possessed same clinically relevant AMR-carrying mobile genetic elements (plasmids: 70.6%, transposons: 78%), which also showed recent common evolution. Machine learning revealed known and novel AMR-associated mutations and genes underlying resistance to 28 antimicrobials and primarily associated with resistance in E. coli and susceptibility in S. enterica. Many were essential and affected same metabolic processes in both species, albeit with varying degrees of phylogenetic penetration. Multi-modal strategies are crucial to investigate the interplay of mobilome, resistance and metabolism in cohabiting bacteria, especially in ecological settings where community-driven resistance selection occurs
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